Xuanyu Chen developed a configurable penalty-control feature for the nv-auto-deploy/TensorRT-LLM repository, enhancing the sampling pipeline to allow users to specify how many prompt tokens are ignored when applying presence and frequency penalties. This addition, implemented in C++ and Python, introduced a new parameter, prompt_ignore_length, to the sampling configuration, enabling more precise model tuning and predictable output behavior across deployments. By focusing on API design and LLM optimization, Xuanyu ensured the feature could be broadly adopted and reused in various deployment scenarios. The work demonstrated a focused, in-depth approach to improving model control without addressing bug fixes this month.